Artificial neural networks and impact of the environmental quality on urban real estate values.

In recent decades, the uncontrolled expansion and increasingly rapid urbanization of cities are leading to dramatic consequences, such as the fragmentation of ecological assets, the loss of biodiversity and, more generally, the deterioration of environmental quality. Therefore, it is becoming increasingly urgent to invest in the conservation and restoration of green areas, so that the urban system itself can in turn become a provider of ecosystem services. Such initiatives, besides generating positive environmental and social benefits, can lead to significant increases in the real estate values of the area involved in urban regeneration. The aim of this paper is therefore to characterise an innovative Artificial Intelligence (AI) model to assess the incidence of urban greening on property prices. Specifically, we propose an Artificial Neural Network (ANN) that includes among its inputs also proxy variables of environmental quality. The ANN, besides being little used to predict property prices, can set up non-linear relationships between inputs and outputs, and returns results that perform better than traditional forecasting models. The critical steps of the model concern the choice of input variables and the setting of the ANN. In the second part of the work, an application to a real case study will allow the model to be tested and demonstrate how measures to preserve green areas can have an impact on both the community and urban real estate. In conclusion, the aim is to show how AI models, also by integrating them with traditional forecasting models, can: on the one hand, provide valuers with a more rigorous set of information on property performance; on the other hand, enable to appreciate the impact of environmental externalities on real estate value, with consequent effects on the whole decision-making process. [ABSTRACT FROM AUTHOR]